Faculty Publications
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Item Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data(Elsevier Ltd, 2018) Areeckal, A.S.; Kamath, J.; Zawadynski, S.; Kocher, M.; Sumam David, S.Osteoporosis is a bone disorder characterized by bone loss and decreased bone strength. The most widely used technique for detection of osteoporosis is the measurement of bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). But DXA scans are expensive and not widely available in low-income economies. In this paper, we propose a low cost pre-screening tool for the detection of low bone mass, using cortical radiogrammetry of third metacarpal bone and trabecular texture analysis of distal radius from hand and wrist radiographs. An automatic segmentation algorithm to automatically locate and segment the third metacarpal bone and distal radius region of interest (ROI) is proposed. Cortical measurements such as combined cortical thickness (CCT), cortical area (CA), percent cortical area (PCA) and Barnett Nordin index (BNI) were taken from the shaft of third metacarpal bone. Texture analysis of trabecular network at the distal radius was performed using features obtained from histogram, gray level Co-occurrence matrix (GLCM) and morphological gradient method (MGM). The significant cortical and texture features were selected using independent sample t-test and used to train classifiers to classify healthy subjects and people with low bone mass. The proposed pre-screening tool was validated on two ethnic groups, Indian sample population and Swiss sample population. Data of 134 subjects from Indian sample population and 65 subjects from Swiss sample population were analysed. The proposed automatic segmentation approach shows a detection accuracy of 86% in detecting the third metacarpal bone shaft and 90% in accurately locating the distal radius ROI. Comparison of the automatic radiogrammetry to the ground truth provided by experts show a mean absolute error of 0.04 mm for cortical width of healthy group, 0.12 mm for cortical width of low bone mass group, 0.22 mm for medullary width of healthy group, and 0.26 mm for medullary width of low bone mass group. Independent sample t-test was used to select the most discriminant features, to be used as input for training the classifiers. Pearson correlation analysis of the extracted features with DXA-BMD of lumbar spine (DXA-LS) shows significantly high correlation values. Classifiers were trained with the most significant features in the Indian and Swiss sample data. Weighted KNN classifier shows the best test accuracy of 78% for Indian sample data and 100% for Swiss sample data. Hence, combined automatic radiogrammetry and texture analysis is shown to be an effective low cost pre-screening tool for early diagnosis of osteoporosis. © 2018 Elsevier LtdItem Fully automatic segmentation of phalanges from hand radiographs for bone age assessment(Taylor and Francis Ltd., 2019) Simu, S.; Lal, S.; Fadte, K.; Harlapur, A.Segmentation of bones from hand radiograph is an important step in automated bone age assessment (ABAA) system. Main challenges in the segmentation of bones are the intensity inhomogeneity caused by the irregular distribution of X-rays and the overlapping pixel intensities between the bone and soft tissue. Hence, there is a need to develop a robust segmentation technique to tackle the problems associated with the hand radiographs. This paper proposes a fully automatic technique for segmentation of phalanges from left-hand radiograph for bone age assessment. The proposed technique is divided into five stages which are pre-processing, extraction of Phalangeal region of interest, edge preservation, segmentation of phalanges and post-processing. Quantitative and qualitative results of proposed segmentation technique are evaluated and compared with other state-of-the-art segmentation methods. Qualitative results of proposed segmentation technique are also validated by different medical experts. The segmentation accuracy achieved by proposed segmentation technique is 94%. The proposed technique can be used for development of fully ABAA of a person for better accuracy. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.Item Phoneme boundary detection from speech: A rule based approach(Elsevier B.V., 2019) Ramteke, P.B.; Koolagudi, S.G.In this paper, a novel approach has been proposed for the automatic segmentation of speech signal into phonemes. In a well spoken word, phonemes can be characterized by the changes observed in speech waveform. To get phoneme boundaries, the signal level properties of speech waveform i.e. changes in the waveform during transformation from one phoneme to the other are explored. The problem of phoneme level segmentation has been addressed in this work from two aspects 1. Segmentation of phonemes between voiced and unvoiced portions and 2. Segmentation of phonemes within voiced and unvoiced regions. Pitch and zero-frequency filter signal are used to get the region of change from voiced to unvoiced and vice versa. The segmentation of phoneme boundaries within voiced and unvoiced regions are approximated using the properties of power spectrum of correlation of adjacent frames of the signal. A finite set of rules is proposed on the variations observed in the power spectrum during phoneme transitions. The segmentation results of both approaches are combined to get the final phoneme boundaries. Three databases namely TIMIT Corpus, IIIT Hyderabad Marathi database & IIIT Hyderabad Hindi database (IIIT-H Indic Speech Databases) are used to test the proposed approach; an accuracy of 95.40%, 96.87% and 96.12% is achieved within the tolerance range of 10 ms respectively. The results of the proposed approach are observed to give precise phoneme boundaries. © 2019 Elsevier B.V.Item Automatic detection and localization of Focal Cortical Dysplasia lesions in MRI using fully convolutional neural network(Elsevier Ltd, 2019) Bijay Dev, K.M.; Pawan, P.S.; Niyas, S.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.Focal cortical dysplasia (FCD) is the leading cause of drug-resistant epilepsy in both children and adults. At present, the only therapeutic approach in patients with drug-resistant epilepsy is surgery. Hence, the quantification of FCD via non-invasive imaging techniques helps physicians to decide on surgical interventions. The properties like non-invasiveness and capability to produce high-resolution images makes magnetic resonance imaging an ideal tool for detecting the FCD to an extent. The FCD lesions vary in size, shape, and location for different patients and make the manual detection time consuming and sensitive to the experience of the observer. Automatic segmentation of FCD lesions is challenging due to the difference in signal strength in images acquired with different machines, noise, and other kinds of distortions such as motion artifacts. Most of the methods proposed in the literature use conventional machine learning and image processing techniques in which their accuracy relies on the trained features. Hence, feature extraction should be done more precisely which requires human expertise. The ability to learn the appropriate features/representations from the training data without any human interventions makes the convolutional neural network (CNN) the suitable method for addressing these drawbacks. As far as we are aware, this work is the first one to use a CNN based model to solve the aforementioned problem using only MRI FLAIR images. We customized the popular U-Net architecture and trained the proposed model from scratch (using MRI images acquired with 1.5T and 3T scanners). FCD detection rate (recall) of the proposed model is 82.5 (33/40 patients detected correctly). © 2019Item Multi-Res-Attention UNet: A CNN Model for the Segmentation of Focal Cortical Dysplasia Lesions from Magnetic Resonance Images(Institute of Electrical and Electronics Engineers Inc., 2021) Thomas, E.; Pawan, S.J.; Kumar, S.; Horo, A.; Niyas, S.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.In this work, we have focused on the segmentation of Focal Cortical Dysplasia (FCD) regions from MRI images. FCD is a congenital malformation of brain development that is considered as the most common causative of intractable epilepsy in adults and children. To our knowledge, the latest work concerning the automatic segmentation of FCD was proposed using a fully convolutional neural network (FCN) model based on UNet. While there is no doubt that the model outperformed conventional image processing techniques by a considerable margin, it suffers from several pitfalls. First, it does not account for the large semantic gap of feature maps passed from the encoder to the decoder layer through the long skip connections. Second, it fails to leverage the salient features that represent complex FCD lesions and suppress most of the irrelevant features in the input sample. We propose Multi-Res-Attention UNet; a novel hybrid skip connection-based FCN architecture that addresses these drawbacks. Moreover, we have trained it from scratch for the detection of FCD from 3 T MRI 3D FLAIR images and conducted 5-fold cross-validation to evaluate the model. FCD detection rate (Recall) of 92% was achieved for patient wise analysis. © 2013 IEEE.